Setting program goals seems like an obvious thing to do for any Customer Marketing Manager. In the beginning, centralizing all your advocate information and tagging it for easy discovery is job #1. You may determine there aren’t enough of a particular type of advocate by segment such as industry, product, use case; or their confirmed commitment to do specific activities (speak, review, reference calls, etc.). You may not trust data you inherited and need to validate the existing data before making it available to stakeholders in PR, Events, Sales, Social, etc.
After tackling what we’d consider the basics, the program can launch (however you define that event). Maybe it’s only supporting Marketing, or Sales, or CABs, or a customer community; or maybe it’s providing services to the full range of functions that rely on advocates.
The first stage of a program, building the foundation, is somewhat linear. There is a sequence of objectives, and until those are achieved, the program is sort of in stealth mode. But after it’s launched there are quite a few activities that have to occur simultaneously, and in-flight. There’s no way to pause any single motion without losing momentum and jeopardizing the program’s success. These balls all have to stay in the air, all of the time.
1) ongoing recruiting
2) data tagging
3) data maintenance
4) managing time-sensitive requests
5) educating/promoting
6) quantifying program value
7) addressing change resistance
Thinking about these operational requirements in a realistic way, is it reasonable to think that they can be addressed one at a time, in sequence, once the program has launched? Hopefully your answer is a resounding “No way!”
That’d be like getting a car on the road but focusing on steering alone, or just the accelerator, or just the brake, or responding to only one road rule at a time, and so on. Not possible. A driver, like a program manager, has to give attention to all these things in order to successfully get from point A to point B.
If you only recruit and build a hefty advocate database, but never promote it to your stakeholders, it’s wasted effort. You’re an advocate collector for the sake of collecting.
If you don’t maintain the robust database you built it will become outdated, and lose its value.
If you make changes (e.g., new processes, new technology, policies) and don’t manage the people side of change, you are setting yourself up for failure. Same goes for new employees, stakeholders of your program. Their acclimation to and embracement of your program has to be an organic part of running your program, not to be ignored. Stakeholders are your raison d’être!
If you buy into the idea that the program manager must constantly attend to a myriad of motions to keep the program humming, then how do you set priorities for what is a finite amount of bandwidth—even more demanding if you’re a team of one?
Your primary mission is to support lead generation, acquisition and, for some, retention. Your company has set growth goals based on attracting and acquiring certain types of accounts. That could be by industry, size, geography, product need or other criteria; or combinations of criteria. All your efforts should be tailored to supporting marketing and sales initiatives, and meeting their respective goals. If you’ve got a database full of the wrong stories, you effectively don’t exist to your stakeholders. Same goes for content. If it’s mis-targeted, time and energy was simply wasted.
Measuring a program’s impact by tracking revenue influenced is fairly standard. It’s straightforward to understand, and there’s a dollar sign involved, which is expected by leadership. They’re measured quantitatively, one way or another.
We view this stat as the outcome, not the source, of a well-run program. The source is activity. You can’t say you want to influence $100 million in revenue without building the scaffolding to get there.
The scaffolding includes the number or percent of events or social posts featuring an advocate, or how advocates speaking at events boosted lead generation. It could be the number or percent of opportunities where advocate content was leveraged or a reference call took place. Initially, these counts might be low (i.e., 15% of opportunities), but your goal is to grow that number to 30%, 50% and beyond. Should you shoot for 100%? Probably not. In the case of opportunity close rates, some just don’t require a reference. Take, for instance, the opportunity that’s with a former client. They already know your brand and your product. They are both the reference and the buyer.
What’s a reasonable target? Remember estimating in grade school? Let’s say you have a sales team of 100 reps. They each have a quota. What percentage of that quota should you hope to influence with high-quality advocates? You have to know what percentage of buyers request references, which may differ by product or deal size. Dive into the particulars, understand the important factors, then and only then, establish meaningful goals.
User adoption of your program is the result of change management. Promotion and education are part of change management. So is the acceptance that people resist change, and people have different reasons. Plan to address the sources of resistance at an individual level. Approaching at a team or division level will only get you so far, never addressing 100% of the resistance. Resistance is not one-size-fits-all.
We find our most successful program manager clients are fearless, action-oriented and persistent when it comes to setting and achieving goals. They are never content. As soon as they knock down a goal, they ratchet it up for the next month, quarter or year. Keep these considerations in mind, and improve your goal attainment. You’ll win executive confidence, support and, deservedly, get the budget you need to take your program to the next level.
And if you need any help getting your program to that next level, we can help!
It's only natural that many advocacy leaders have landed on the same objective: make the program easier to use by meeting users where they're already working.
Today, that increasingly means Microsoft Copilot, ChatGPT, Claude, Gemini or whatever generative AI assistant employees happen to have open.
Imagine a salesperson simply asking AI, "Find me three German healthcare customers using product Y, willing to speak with a prospect," instead of navigating to another interface, or waiting for someone from advocacy, or elsewhere, to respond. It's easy to see the appeal. Removing friction has always been one of the fastest ways to increase adoption.
It is exactly the right instinct.
The difficult parts, arguably the reason program managers exist, occur before and after AI says, "Here are your three best matches."
The value advocacy professionals bring is the ability to operationalize and scale customer advocacy for maximum impact. Quality advocate information doesn't just appear, it's the result of a system.
Now that the user has three advocates, what should happen?
Notice what happened. The search was completed.
The next steps are just as manual as ever if AI search is the be all, end all.
Reality Check
AI can tell you who could participate. It can't tell you who should participate unless someone (or something) has been keeping score.
This is where the story starts to feel strangely familiar.
Many companies still operate their program using spreadsheets, scattered CRM fields, shared drives, email folders, and the remarkable memories of a handful of program managers.
Eventually, organizations realize they aren't managing an advocacy program at all. They're managing lists that happen to contain advocates.
But the shortcomings are real:
Purpose-built advocacy platforms emerged because advocacy is much more than a search problem.
Ironically, AI has convinced some organizations to revisit the same shortcut they worked so hard to escape.
Let's imagine two different worlds.
In the first, AI recommends an advocate for a sales call.
Months later, AI knows this customer recently participated and may deserve a break before being asked again.
Now imagine the second world.
Three months later someone asks how many customer reference contributed to the revenue this quarter.
Silence. Nobody really knows.
The advocacy happened...hopefully. The program didn't. Collectively, the organization slowly stopped feeding the very system it depended on to understand its advocacy program.
Reality Check
If AI helps facilitate twenty closed-won opportunities this quarter, but none are recorded, your executive dashboard still says zero.
One of the easiest mistakes to make in an AI-first world is assuming that successful interactions somehow become organizational knowledge on their own.
They don't.
If a customer agrees to speak with a prospect and nobody records it, the organization loses far more than a single activity.
The most valuable advocacy data isn't simply who your customers are.
It's everything they've done.
That's the story AI actually wants to read.
It's often said that AI needs good data.
That's true.
But operational history is far more valuable than static customer information.
Those aren't search results.Those are patterns.
Remove any one of those pieces and AI becomes little more than an exceptionally fast search engine.
Reality Check
Every workflow skipped today is a pattern AI won't discover tomorrow.
The AI revolution has created tremendous excitement, and rightly so. Finding the right advocate is becoming dramatically easier than it was only a few years ago.
That's worth celebrating.
Just don't confuse a better search experience with a better advocacy program. Search is only one chapter in the story.
The organizations that see the greatest return from AI won't necessarily be the ones with the most sophisticated models.
They'll be the ones with the richest operational history.
Those organizations won't use AI merely to answer the question, "Who should we ask?"
They'll use AI to answer far more valuable questions.
That's when AI stops behaving like a better Google search.
That's when it starts behaving like a strategic partner.
Finding the right advocate has always been the opening scene.
If your AI can find advocates but your program can't learn from using them, you've built a remarkable search engine instead of a remarkable advocacy program.
.svg){kind=icon}
